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Object matching or object consolidation is a crucial task for data integration
and data cleaning. It addresses the problem of identifying
object instances in data sources referring to the same real world
entity. We propose a flexible framework called MOMA for mapping-
based object matching. It allows the construction of match
workflows combining the results of several matcher algorithms on
both attribute values and contextual information. The output of a
match task is an instance-level mapping that supports information
fusion in P2P data integration systems and can be re-used for other
match tasks. MOMA utilizes further semantic mappings of different
cardinalities and provides merge and compose operators for mapping
combination. We propose and evaluate several strategies for
both object matching between different sources as well as for duplicate
identification within a single data source.